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+Base class for all TensorFlow estimators.
+
+Parameters:
+  model_fn: Model function, that takes input X, y tensors and outputs
+    prediction and loss tensors.
+  n_classes: Number of classes in the target.
+  batch_size: Mini batch size.
+  steps: Number of steps to run over data.
+  optimizer: Optimizer name (or class), for example "SGD", "Adam",
+    "Adagrad".
+  learning_rate: If this is constant float value, no decay function is used.
+    Instead, a customized decay function can be passed that accepts
+    global_step as parameter and returns a Tensor.
+    e.g. exponential decay function:
+    def exp_decay(global_step):
+        return tf.train.exponential_decay(
+            learning_rate=0.1, global_step,
+            decay_steps=2, decay_rate=0.001)
+  clip_gradients: Clip norm of the gradients to this value to stop
+    gradient explosion.
+  class_weight: None or list of n_classes floats. Weight associated with
+    classes for loss computation. If not given, all classes are supposed to
+    have weight one.
+  continue_training: when continue_training is True, once initialized
+    model will be continuely trained on every call of fit.
+  config: RunConfig object that controls the configurations of the
+    session, e.g. num_cores, gpu_memory_fraction, etc.
+  verbose: Controls the verbosity, possible values:
+    0: the algorithm and debug information is muted.
+    1: trainer prints the progress.
+    2: log device placement is printed.
+- - -
+
+#### `tf.contrib.learn.TensorFlowEstimator.__init__(model_fn, n_classes, batch_size=32, steps=200, optimizer='Adagrad', learning_rate=0.1, clip_gradients=5.0, class_weight=None, continue_training=False, config=None, verbose=1)` {#TensorFlowEstimator.__init__}
+
+
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowEstimator.evaluate(x=None, y=None, input_fn=None, steps=None)` {#TensorFlowEstimator.evaluate}
+
+See base class.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowEstimator.fit(x, y, steps=None, monitors=None, logdir=None)` {#TensorFlowEstimator.fit}
+
+Builds a neural network model given provided `model_fn` and training
+data X and y.
+
+Note: called first time constructs the graph and initializers
+variables. Consecutives times it will continue training the same model.
+This logic follows partial_fit() interface in scikit-learn.
+
+To restart learning, create new estimator.
+
+##### Args:
+
+
+*  <b>`x`</b>: matrix or tensor of shape [n_samples, n_features...]. Can be
+  iterator that returns arrays of features. The training input
+  samples for fitting the model.
+
+*  <b>`y`</b>: vector or matrix [n_samples] or [n_samples, n_outputs]. Can be
+  iterator that returns array of targets. The training target values
+  (class labels in classification, real numbers in regression).
+
+*  <b>`steps`</b>: int, number of steps to train.
+         If None or 0, train for `self.steps`.
+*  <b>`monitors`</b>: List of `BaseMonitor` objects to print training progress and
+    invoke early stopping.
+*  <b>`logdir`</b>: the directory to save the log file that can be used for
+  optional visualization.
+
+##### Returns:
+
+  Returns self.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowEstimator.get_params(deep=True)` {#TensorFlowEstimator.get_params}
+
+Get parameters for this estimator.
+
+##### Args:
+
+
+*  <b>`deep`</b>: boolean, optional
+    If True, will return the parameters for this estimator and
+    contained subobjects that are estimators.
+
+##### Returns:
+
+  params : mapping of string to any
+  Parameter names mapped to their values.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowEstimator.get_tensor(name)` {#TensorFlowEstimator.get_tensor}
+
+Returns tensor by name.
+
+##### Args:
+
+
+*  <b>`name`</b>: string, name of the tensor.
+
+##### Returns:
+
+  Tensor.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowEstimator.get_tensor_value(name)` {#TensorFlowEstimator.get_tensor_value}
+
+Returns value of the tensor give by name.
+
+##### Args:
+
+
+*  <b>`name`</b>: string, name of the tensor.
+
+##### Returns:
+
+  Numpy array - value of the tensor.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowEstimator.get_variable_names()` {#TensorFlowEstimator.get_variable_names}
+
+Returns list of all variable names in this model.
+
+##### Returns:
+
+  List of names.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowEstimator.model_dir` {#TensorFlowEstimator.model_dir}
+
+
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowEstimator.partial_fit(x, y)` {#TensorFlowEstimator.partial_fit}
+
+Incremental fit on a batch of samples.
+
+This method is expected to be called several times consecutively
+on different or the same chunks of the dataset. This either can
+implement iterative training or out-of-core/online training.
+
+This is especially useful when the whole dataset is too big to
+fit in memory at the same time. Or when model is taking long time
+to converge, and you want to split up training into subparts.
+
+##### Args:
+
+
+*  <b>`x`</b>: matrix or tensor of shape [n_samples, n_features...]. Can be
+  iterator that returns arrays of features. The training input
+  samples for fitting the model.
+
+*  <b>`y`</b>: vector or matrix [n_samples] or [n_samples, n_outputs]. Can be
+  iterator that returns array of targets. The training target values
+  (class label in classification, real numbers in regression).
+
+##### Returns:
+
+  Returns self.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowEstimator.predict(x, axis=1, batch_size=None)` {#TensorFlowEstimator.predict}
+
+Predict class or regression for X.
+
+For a classification model, the predicted class for each sample in X is
+returned. For a regression model, the predicted value based on X is
+returned.
+
+##### Args:
+
+
+*  <b>`x`</b>: array-like matrix, [n_samples, n_features...] or iterator.
+*  <b>`axis`</b>: Which axis to argmax for classification.
+    By default axis 1 (next after batch) is used.
+    Use 2 for sequence predictions.
+*  <b>`batch_size`</b>: If test set is too big, use batch size to split
+    it into mini batches. By default the batch_size member
+    variable is used.
+
+##### Returns:
+
+
+*  <b>`y`</b>: array of shape [n_samples]. The predicted classes or predicted
+  value.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowEstimator.predict_proba(x, batch_size=None)` {#TensorFlowEstimator.predict_proba}
+
+Predict class probability of the input samples X.
+
+##### Args:
+
+
+*  <b>`x`</b>: array-like matrix, [n_samples, n_features...] or iterator.
+*  <b>`batch_size`</b>: If test set is too big, use batch size to split
+    it into mini batches. By default the batch_size member variable is used.
+
+##### Returns:
+
+
+*  <b>`y`</b>: array of shape [n_samples, n_classes]. The predicted
+  probabilities for each class.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowEstimator.restore(cls, path, config=None)` {#TensorFlowEstimator.restore}
+
+Restores model from give path.
+
+##### Args:
+
+
+*  <b>`path`</b>: Path to the checkpoints and other model information.
+*  <b>`config`</b>: RunConfig object that controls the configurations of the session,
+    e.g. num_cores, gpu_memory_fraction, etc. This is allowed to be
+      reconfigured.
+
+##### Returns:
+
+  Estimator, object of the subclass of TensorFlowEstimator.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowEstimator.save(path)` {#TensorFlowEstimator.save}
+
+Saves checkpoints and graph to given path.
+
+##### Args:
+
+
+*  <b>`path`</b>: Folder to save model to.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowEstimator.set_params(**params)` {#TensorFlowEstimator.set_params}
+
+Set the parameters of this estimator.
+
+The method works on simple estimators as well as on nested objects
+(such as pipelines). The former have parameters of the form
+``<component>__<parameter>`` so that it's possible to update each
+component of a nested object.
+
+##### Returns:
+
+  self
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowEstimator.train(input_fn, steps, monitors=None)` {#TensorFlowEstimator.train}
+
+Trains a model given input builder function.
+
+##### Args:
+
+
+*  <b>`input_fn`</b>: Input builder function, returns tuple of dicts or
+            dict and Tensor.
+*  <b>`steps`</b>: number of steps to train model for.
+*  <b>`monitors`</b>: List of `BaseMonitor` subclass instances. Used for callbacks
+            inside the training loop.
+
+##### Returns:
+
+  Returns self.
+
+